Users of modern portable consumer devices (smartphones, tablets etc.) expect ubiquitous delivery of high quality services, which fully utilise the capabilities of their devices. Video streaming is one of the most widely used yet challenging services for operators to deliver with assured service levels. This challenge is more apparent in wireless networks where bandwidth constraints and packet loss are common. The lower bandwidth requirements of High Efficiency Video Coding (HEVC) provide the potential to enable service providers to deliver high quality video streams in low-bandwidth networks; however, packet loss may result in greater damage in perceived quality given the higher compression ratio. This work considers the delivery of HEVC encoded video streams in impaired network environments and quantifies the effects of network impairment on HEVC video streaming from the perspective of the end user. HEVC encoded streams were transmitted over a test network with both wired and wireless segments that had imperfect communication channels subject to packet loss. Two different error concealment methods were employed to mitigate packet loss and overcome reference decoder robustness issues. The perceptual quality of received video was subjectively assessed by a panel of viewers. Existing subjective studies of HEVC quality have not considered the implications of network impairments. Analysis of results has quantified the effects of packet loss in HEVC on perceptual quality and provided valuable insight into the relative importance of the main factors observed to influence user perception in HEVC streaming. The outputs from this study show the relative importance and relationship between those factors that affect human perception of quality in impaired HEVC encoded video streams. The subjective analysis is supported by comparison with commonly used objective quality measurement techniques. Outputs from this work may be used in the development of quality of experience (QoE) oriented streaming applications for HEVC in loss prone networks.